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Cholesky factorization
T = chol(A)
[T,p] =
chol(A)
[T,p,S]
= chol(A)
[T,p,s]
= chol(A,'vector')
___ = chol(A,'lower')
___ = chol(A,'noCheck')
___ = chol(A,'real')
___ = chol(A,'lower','noCheck','real')
[T,p,s]
= chol(A,'lower','vector','noCheck','real')
T = chol(A) returns an upper triangular matrix T, such that T'*T = A. A must be a Hermitian positive definite matrix. Otherwise, this syntax throws an error.
[T,p] = chol(A) computes the Cholesky factorization of A. This syntax does not error if A is not a Hermitian positive definite matrix. If A is a Hermitian positive definite matrix, then p is 0. Otherwise, T is sym([]), and p is a positive integer (typically, p = 1).
[T,p,S] = chol(A) returns a permutation matrix S, such that T'*T = S'*A*S, and the value p = 0 if matrix A is Hermitian positive definite. Otherwise, it returns a positive integer p and an empty symbolic object S = sym([]).
[T,p,s] = chol(A,'vector') returns the permutation information as a vector s, such that A(s,s) = T'*T. If A is not recognized as a Hermitian positive definite matrix, then p is a positive integer and s = sym([]).
___ = chol(A,'lower') returns a lower triangular matrix T, such that T*T' = A.
___ = chol(A,'noCheck') skips checking whether matrix A is Hermitian positive definite. 'noCheck' lets you compute Cholesky factorization of a matrix that contains symbolic parameters without setting additional assumptions on those parameters.
___ = chol(A,'real') computes the Cholesky factorization of A using real arithmetic. In this case, chol computes a symmetric factorization A = T.'*T instead of a Hermitian factorization A = T'*T. This approach is based on the fact that if A is real and symmetric, then T'*T = T.'*T. Use 'real' to avoid complex conjugates in the result.
___ = chol(A,'lower','noCheck','real') computes the Cholesky factorization of A with one or more of these optional arguments: 'lower', 'noCheck', and 'real'. These optional arguments can appear in any order.
[T,p,s] = chol(A,'lower','vector','noCheck','real') computes the Cholesky factorization of A and returns the permutation information as a vector s. You can use one or more of these optional arguments: 'lower', 'noCheck', and 'real'. These optional arguments can appear in any order.
A |
Symbolic matrix. |
'lower' |
Flag that prompts chol to return a lower triangular matrix instead of an upper triangular matrix. |
'vector' |
Flag that prompts chol to return the permutation information in the form of a vector. To use this flag, you must specify three output arguments. |
'noCheck' |
Flag that prompts chol to avoid checking whether matrix A is Hermitian positive definite. Use this flag if A contains symbolic parameters, and you want to avoid additional assumptions on these parameters. |
'real' |
Flag that prompts chol to use real arithmetic. Use this flag if A contains symbolic parameters, and you want to avoid complex conjugates. |
T |
Upper triangular matrix, such that T'*T = A, or lower triangular matrix, such that T*T' = A. |
p |
Value 0 if A is Hermitian positive definite or if you use 'noCheck'. If chol does not identify A as a Hermitian positive definite matrix, then p is a positive integer. R is an upper triangular matrix of order q = p - 1, such that R'*R = A(1:q,1:q). |
S |
Permutation matrix. |
s |
Permutation vector. |
Compute the Cholesky factorization of the 3-by-3 Hilbert matrix. Because these numbers are not symbolic objects, you get floating-point results.
chol(hilb(3))
ans = 1.0000 0.5000 0.3333 0 0.2887 0.2887 0 0 0.0745
Now convert this matrix to a symbolic object, and compute the Cholesky factorization:
chol(sym(hilb(3)))
ans = [ 1, 1/2, 1/3] [ 0, 3^(1/2)/6, 3^(1/2)/6] [ 0, 0, 5^(1/2)/30]
Compute the Cholesky factorization of the 3-by-3 Pascal matrix returning a lower triangular matrix as a result:
chol(sym(pascal(3)), 'lower')
ans = [ 1, 0, 0] [ 1, 1, 0] [ 1, 2, 1]
Try to compute the Cholesky factorization of this matrix. Because this matrix is not Hermitian positive definite, chol used without output arguments or with one output argument throws an error:
A = sym([1 1 1; 1 2 3; 1 3 5]);
T = chol(A)
Error using sym/chol (line 132) Cannot prove that input matrix is Hermitian positive definite. Define a Hermitian positive definite matrix by setting appropriate assumptions on matrix components, or use 'noCheck' to skip checking whether the matrix is Hermitian positive definite.
To suppress the error, use two output arguments, T and p. If the matrix is not recognized as Hermitian positive definite, then this syntax assigns an empty symbolic object to T and the value 1 to p:
[T,p] = chol(A)
T = [ empty sym ] p = 1
For a Hermitian positive definite matrix, p is 0:
[T,p] = chol(sym(pascal(3)))
T = [ 1, 1, 1] [ 0, 1, 2] [ 0, 0, 1] p = 0
Compute the Cholesky factorization of the 3-by-3 inverse Hilbert matrix returning the permutation matrix:
A = sym(invhilb(3)); [T, p, S] = chol(A)
T = [ 3, -12, 10] [ 0, 4*3^(1/2), -5*3^(1/2)] [ 0, 0, 5^(1/2)] p = 0 S = [ 1, 0, 0] [ 0, 1, 0] [ 0, 0, 1]
Compute the Cholesky factorization of the 3-by-3 inverse Hilbert matrix returning the permutation information as a vector:
A = sym(invhilb(3)); [T, p, S] = chol(A, 'vector')
T = [ 3, -12, 10] [ 0, 4*3^(1/2), -5*3^(1/2)] [ 0, 0, 5^(1/2)] p = 0 S = [ 1, 2, 3]
Compute the Cholesky factorization of matrix A containing symbolic parameters. Without additional assumptions on the parameter a, this matrix is not Hermitian. To make isAlways return logical 0 (false) for undecidable conditions, set Unknown to false.
syms a A = [a 0; 0 a]; isAlways(A == A','Unknown','false')
ans = 0 1 1 0
By setting assumptions on a and b, you can define A to be Hermitian positive definite. Therefore, you can compute the Cholesky factorization of A:
assume(a > 0) chol(A)
ans = [ a^(1/2), 0] [ 0, a^(1/2)]
For further computations, remove the assumptions:
syms a clear
'noCheck' lets you skip checking whether A is a Hermitian positive definite matrix. Thus, this flag lets you compute the Cholesky factorization of a symbolic matrix without setting additional assumptions on its components:
A = [a 0; 0 a]; chol(A,'noCheck')
ans = [ a^(1/2), 0] [ 0, a^(1/2)]
If you use 'noCheck' for computing the Cholesky factorization of a matrix that is not Hermitian positive definite, chol can return a matrix T for which the identity T'*T = A does not hold. To make isAlways return logical 0 (false) for undecidable conditions, set Unknown to false.
T = chol(sym([1 1; 2 1]), 'noCheck')
T = [ 1, 2] [ 0, 3^(1/2)*i]
isAlways(A == T'*T,'Unknown','false')
ans = 0 0 0 0
Compute the Cholesky factorization of this matrix. To skip checking whether it is Hermitian positive definite, use 'noCheck'. By default, chol computes a Hermitian factorization A = T'*T. Thus, the result contains complex conjugates.
syms a b A = [a b; b a]; T = chol(A, 'noCheck')
T = [ a^(1/2), conj(b)/conj(a^(1/2))] [ 0, (a*abs(a) - abs(b)^2)^(1/2)/abs(a)^(1/2)]
To avoid complex conjugates in the result, use 'real':
T = chol(A, 'noCheck', 'real')
T = [ a^(1/2), b/a^(1/2)] [ 0, ((a^2 - b^2)/a)^(1/2)]
When you use this flag, chol computes a symmetric factorization A = T.'*T instead of a Hermitian factorization A = T'*T. To make isAlways return logical 0 (false) for undecidable conditions, set Unknown to false.
isAlways(A == T.'*T)
ans = 1 1 1 1
isAlways(A == T'*T,'Unknown','false')
ans = 0 0 0 0
chol | ctranspose | eig | isAlways | linalg::factorCholesky | linalg::isHermitian | linalg::isPosDef | lu | qr | svd | transpose | vpa